Vision Transformer-based Approach for Solder Mask Fault Detection

Quality control is a paramount task for the manufacturing process at the current time. Nowadays, industrial companies must guarantee the quality of their products to keep competitiveness. This paper addresses the problem of controlling the quality of GSM chips during the soldering process of individual chips in PCB substrates, providing quality to the manufacturing stage. We propose an approach to detect solder mask faults in GSM chips, during the soldering stage. The proposed methodology is based on the use of Vision Transformers (ViT), which use self-attention mechanisms to capture the spatial relationships between image patches, allowing the model to learn hierarchical visual representations. Real and simulated experiments were carried out to validate the proposed approach. Results show the obtained accuracy of 95.83%, using the proposed ViT-based inspection approach. Furthermore, the proposed approach presents high accuracy even regarding noisy and blurry images, resulting in an accuracy of 94.94% and 94.17% for Salt and Pepper and Gaussian noise, respectively, in the worst scenario. Experiments demonstrate reliability and robustness, optimizing the manufacturing.